一个数据科学路线图,用于参与早期药物发现的开放科学组织
Kristina Edfeldt1, Aled M Edwards2, Ola Engkvist3
1Structural Genomics Consortium, Department of Medicine, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden.
Nature communications
|July 4, 2024
概括
人工智能 (AI) 可以加速药物发现. 建议侧重于强大的数据管理,开放共享,并将AI整合到实验设计中,以更快地发现和开发化学探测器.
科学领域:
- 药物发现 药物发现 药物发现
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 结构基因组学联盟加速早期药物发现,专注于成功发现和优化.
- 人工智能 (AI) 被认为是现代药物发现的关键加速器.
- 需要优化数据生成,格式化和传播,以实现人工智能引导的药物发现.
研究的目的:
- 提出公私专家工作组关于利用人工智能用于药物发现的建议.
- 概述数据管理,共享和AI模型开发的策略.
- 确定加速打击和化学探测器发现的关键因素.
主要方法:
- 专家工作组的共识和建议.
- 强调强大的数据管理与本体学和标准化词汇.
- 整合实验室自动化,电子实验室笔记本和云计算,用于数据共享和AI模型开发.
主要成果:
- 集中式数据库架构促进了高价值数据集的创建.
- 通过自动化实验室流程开放的数据共享和数据挖掘至关重要.
- 透明的数据处理,适当的数据表示和严格的模型验证对于机器学习至关重要.
结论:
- 在设计-制造-测试-分析 (DMTA) 周期中实时整合实验数据和AI建模至关重要.
- 促进数据科学家和实验人员之间的合作是关键.
- 采用统一的团队方法,并将数据科学纳入实验设计将加速发现.
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